This invention is a method for ranking politically exposed persons (“PEPs”) and/or other persons and entities that pose a heightened compliance, legal, regulatory, and reputation risk to financial institutions (e.g. known and suspected criminals and their associates) based on their centrality or “importance” within certain graph representations of the watch list databases in which are profiled. The rankings provide for an objective measure of the underlying risk posed by individual PEP's and other heightened-risk entities relative to others in the same watch list database source. The invention is considered to be particularly useful to financial institutions in screening potential and existing clients.
In the international financial industry, there is a general consensus that senior political figures, along with their family members and known close associates, collectively referred to as “Politically Exposed Persons” or “PEPs”—represent an inherent heightened risk given their direct or indirect access to public funds and influence over the business and commercial affairs in their jurisdictions. Consequently, most financial institutions are counseled, and in many cases required, to mitigate the risk of becoming wittingly or unwittingly complicit in the furtherance of political corruption by performing the following steps: 1) proactively identifying customers who are PEPs, 2) comprehensively assessing the acceptability of the specific risk they pose and, 3) if deemed to pose an acceptable risk, placing their accounts under continuing heightened scrutiny and periodic re-assessment.
For the first step, proactive identification, many financial institutions automatically screen their client databases against subscription-based commercial PEP watch list databases that are themselves compiled from public sources such as government, law enforcement and news media websites. Many of these databases, since coming into being, have augmented their coverage beyond PEPs to include other categories of risk (e.g. fraud, money laundering, narcotics crime, terrorism, etc.) and are often referred to as “PEP/KYC databases” (“KYC” is an acronym for “Know Your Customer”, industry parlance for general customer due diligence policies). Records of related persons and entities in PEP/KYC databases typically link to one another.
The screening of large target client databases against large source PEP/KYC databases inevitably leads to the problem of false positive matches given the public nature of the source data; PEP/KYC database records rarely provide unique identifiers such as social security numbers and often do not provide strong ones such as date of birth. Thus, most matches are generated based solely on the similarity between target and source names and can be properly viewed only as a starting point for investigating whether the two matching names represent the same person or entity.
In wide-recognition of the infeasibility of investigating all potential matches, there is a consensus in favor of a risk-based approach where priority is given to matches against the highest-risk PEP/KYC records. Such an approach requires some method for grouping or ranking PEP/KYC records by risk. The conventional method makes use of the various category and attribute fields available in records such as risk type (e.g. PEP, fraud, narcotics, terrorism), political position (e.g. Presidents, Governors, Mayors) and country of origin to create risk groups (e.g. Presidents from Countries X and Y are a high-risk group; Mayor from Country Z, a low-risk one). A related variant is a rudimentary risk scoring scheme where category values are assigned scores (e.g. Presidents=3, Governors=2, Mayors=1, Country X=3, Country Y=2, Country Z=1) that are aggregated to derive profile scores (e.g. Presidents from Country X=3×3=9, Mayors from Country Z=1×1=1).
These top-down, category-based methods come in infinite varieties but operate from the same overly simplistic premise: namely that the level of risk that best characterizes a general class of PEP/KYC records adequately characterizes each individual member of that class. Local officials from highly developed countries, for example, may be best characterized as low risk as a general class. It does not follow, however, that each individual local official from a highly developed country is adequately characterized as low risk. It certainly does not adequately characterize the local official, for example, who happens to be the related to a prominent national political figure or notorious criminal or who is reported in the news to be under suspicion for corruption.
The example above alludes to a more useful alternative approach where PEP/KYC records are not evaluated based on their membership to a general class (e.g. local officials) but based on their particular connections to relevant entities (e.g. ties to a prominent national figure or a notorious criminal, or a news article on corruption).